About the tech talk: Music information retrieval (MIR) is an interdisciplinary field bridging the domains of statistics, signal processing, machine learning, musicology, biology, and more. MIR algorithms allow a computer to make sense of audio data in order to bridge the semantic gap between high-level musical information — e.g. tempo, key, pitch, instrumentation, chord progression, genre, song structure — and low-level audio data.

In this talk, Steve surveys common research problems in MIR, including music fingerprinting, transcription, classification, and recommendation, and recently proposed solutions in the research literature. The talk contains both a high-level overview as well as concrete examples of implementing MIR algorithms in Python using NumPy, SciPy, and the IPython notebook.

About the speaker: Steve Tjoa is a researcher and engineer in the areas of signal processing and machine learning for music information retrieval (MIR). He currently works on the MIR team at Humtap. Before that, he worked on content-based audio recognition and recommendation as an NSF-sponsored postdoctoral fellow at iZotope and Imagine Research (acquired by iZotope). After earning a PhD in electrical engineering from the University of Maryland in 2011, Steve has been the co-instructor for the annual summer workshop on MIR at Stanford University. Follow him on Twitter at @stevetjoa.

Data is deeply integrated into decision-making companies. But businesses have difficulty understanding the glut of data they now create and consume. What is all this data, and how do we use it?

Clare covers how to get started with data scripting (a powerful tool for prototyping data work). Data skills are crucial as engineering teams are responsible for discovering, housing, and extracting business-critical data.

Learn how to find meaningful data, develop heuristics for normalizing and slicing data, and define useful data structures. Clare utilizes the pythonic Data Scientists’ favorite tools, numpy, pandas, and iPython. Finally, she talks about data work in industry, and why your scripting skills will be a superpower in a data-rich world.

About the speaker: Clare Corthell is a Data Scientist and Designer at Mattermark, a data-driven deal intelligence platform, where she builds technologies that quantify the growth of private companies. She is the originator of The Open Source Data Science Masters, a curriculum for learning Data Science. A Stanford-trained product designer and engineer, she’s founded and worked with early-stage companies in the US, Europe, and East Africa. She’s up early pondering discovery algorithms, information design, diglossia, and education systems. Follow her on Twitter at @clarecorthell.

Giving the tech talk is Christine Yen, a software engineer at Parse (acquired by Facebook last year). She talks about how to pimp out your git config, build customized widgets for your desktop, hook up crazy workflows to system hotkeys, and more. There will be a bit of bash scripting basics, and hopefully you’ll walk away with ideas on how to take the problem-solving skills you’ve developed and apply them to situations outside of work and your side projects. Not everything you code needs to be serious or monolithic – sprinkling a little bit of code here and there can make all sorts of magic happen.

About the speaker: Christine Yen works as a Software Engineer at Parse (Facebook). After starting a Y Combinator startup and working on consumer-facing problems across the stack at Aardvark and Google, she’s excited to be building something developers love. Christine is happiest when neck-deep in an interesting project. She graduated from MIT with a B.S. in Computer Science, tries to read a book a week, loves commas (and parentheticals), and is surprisingly crafty. Follow her on Twitter at @cyen.